Deepankar,
If the problem seems to be in the evaluation of numerical quadrature part,
you might want to try quadrature methods that are better suited to
integrands with strong peaks. The traditional Gaussian quadrature methods,
even their adaptive versions such as Gauss-Kronrod, are not best
Ravi,
Thanks a lot for your detailed suggestions. I will certainly look at the
links that you have sent and the package mnormt. For the moment, I
have managed to analytically integrate the expression using pnorm
along the lines suggested by Prof. Ripley yesterday.
For instance, my first
Hi R users,
I have a couple of questions about some problems that I am facing with
regard to numerical integration and optimization of likelihood
functions. Let me provide a little background information: I am trying
to do maximum likelihood estimation of an econometric model that I have
Deepankar Basu basu.15 at osu.edu writes:
For my model, the likelihood function for each observation is the sum of
three integrals. The integrand in each of these integrals is of the
following form:
A*exp(B+C*x-D*x^2)
(where D is positive)
Being very lazy, I tried Mathematica's
You are trying to use a derivative-based optimization method without
supplying derivatives. This will use numerical approoximations to the
derivatives, and your objective function will not be suitable as it is
internally using adaptive numerical quadrature and hence is probably not
close
Prof. Ripley,
The code that I provided with my question of course does not contain
code for the derivatives; but I am supplying analytical derivatives in
my full program. I did not include that code with my question because
that would have added about 200 more lines of code without adding any
new
